Reducing the computational cost for sound classification in hearing aids by selecting features via genetic algorithms with restricted search

This paper centers on designing a feature-selection algorithm able to provide a ldquosmallrdquo number of adequate features that assist a sound classification system for hearing aids in reducing its computational load without degrading its performance. Because of the problem complexity, we have explored the use of genetic algorithms with restricted search for the mentioned feature selection. In an effort to evaluate its performance, the algorithm has been compared to a standard unconstrained genetic algorithm and with sequential methods. The restricted search driven by the proposed algorithm performs better than both the sequential methods and unconstrained genetic algorithms. The proposed algorithm selects a feature subset composed of only 21 features, much smaller than the 76 features of the complete, original set of available features. This low-cardinality subset of signal-describing features is the one implemented on the hearing aid, saving thus a great number of the scarce computational resources, and making possible to put into practice the concept at reasonable cost.

[1]  Enrique Alexandre,et al.  Automatic Sound Classification for Improving Speech Intelligibility in Hearing Aids Using a Layered Structure , 2006, IDEAL.

[2]  Enric Guaus,et al.  A Non-linear Rhythm-Based Style Classifciation for Broadcast Speech-Music Discrimination , 2004 .

[3]  Enrique Alexandre,et al.  Feature Selection for Sound Classification in Hearing Aids Through Restricted Search Driven by Genetic Algorithms , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  Kari Torkkola,et al.  On feature extraction by mutual information maximization , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Norbert Dillier,et al.  Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis , 2005, EURASIP J. Adv. Signal Process..

[6]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[7]  E. Oja,et al.  Independent Component Analysis , 2001 .

[8]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[9]  William M. Campbell,et al.  Mutual Information in Learning Feature Transformations , 2000, ICML.

[10]  Christine M. Anderson-Cook.  Practical Genetic Algorithms (2nd ed.) , 2005 .

[11]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[12]  Lie Lu,et al.  Content analysis for audio classification and segmentation , 2002, IEEE Trans. Speech Audio Process..

[13]  Fernando Pérez-Cruz,et al.  Enhancing genetic feature selection through restricted search and Walsh analysis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  F. Lopez-Ferreras,et al.  Application of Fisher Linear Discriminant Analysis to Speech/Music Classification , 2006, EUROCON 2005 - The International Conference on "Computer as a Tool".

[15]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[16]  Paul Mermelstein,et al.  Experiments in syllable-based recognition of continuous speech , 1980, ICASSP.

[17]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[18]  Malcolm Slaney,et al.  Construction and evaluation of a robust multifeature speech/music discriminator , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[21]  Michael Christoph Büchler,et al.  Algorithms for sound classification in hearing instruments , 2002 .